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        "%matplotlib inline"
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      "source": [
        "\n# Decision Tree Regression with AdaBoost\n\n\nA decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D\nsinusoidal dataset with a small amount of Gaussian noise.\n299 boosts (300 decision trees) is compared with a single decision tree\nregressor. As the number of boosts is increased the regressor can fit more\ndetail.\n\n.. [1] H. Drucker, \"Improving Regressors using Boosting Techniques\", 1997.\n\n\n"
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    {
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      "execution_count": null,
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      "source": [
        "print(__doc__)\n\n# Author: Noel Dawe <noel.dawe@gmail.com>\n#\n# License: BSD 3 clause\n\n# importing necessary libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import AdaBoostRegressor\n\n# Create the dataset\nrng = np.random.RandomState(1)\nX = np.linspace(0, 6, 100)[:, np.newaxis]\ny = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])\n\n# Fit regression model\nregr_1 = DecisionTreeRegressor(max_depth=4)\n\nregr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),\n                          n_estimators=300, random_state=rng)\n\nregr_1.fit(X, y)\nregr_2.fit(X, y)\n\n# Predict\ny_1 = regr_1.predict(X)\ny_2 = regr_2.predict(X)\n\n# Plot the results\nplt.figure()\nplt.scatter(X, y, c=\"k\", label=\"training samples\")\nplt.plot(X, y_1, c=\"g\", label=\"n_estimators=1\", linewidth=2)\nplt.plot(X, y_2, c=\"r\", label=\"n_estimators=300\", linewidth=2)\nplt.xlabel(\"data\")\nplt.ylabel(\"target\")\nplt.title(\"Boosted Decision Tree Regression\")\nplt.legend()\nplt.show()"
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